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    A Context-Sensitive Generalization of ICA


    Pearlmutter, Barak A. and Parra, Lucas C. (1996) A Context-Sensitive Generalization of ICA. Advances in Neural Information Processing Systems, 151. ISSN 1049-5258

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    Abstract

    Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the result of mixing n unknown independent sources through an unknown n x n mixing matrix. The recently introduced ICA blind source separation algorithm (Baram and Roth 1994; Bell and Sejnowski 1995) is a powerful and surprisingly simple technique for solving this problem. ICA is all the more remarkable for performing so well despite making absolutely no use of the temporal structure of its input! This paper presents a new algorithm, contextual ICA, which derives from a maximum likelihood density estimation formulation of the problem. cICA can incorporate arbitrarily complex adaptive history-sensitive source models, and thereby make use of the temporal structure of its input. This allows it to separate in a number of situations where standard ICA cannot, including sources with low kurtosis, colored gaussian sources, and sources which have gaussian histograms. Since ICA is a special case of cICA, the MLE derivation provides as a corollary a rigorous derivation of classic ICA.

    Item Type: Article
    Keywords: Context-Sensitive; Generalization of ICA;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5491
    Depositing User: Barak Pearlmutter
    Date Deposited: 14 Oct 2014 14:49
    Journal or Publication Title: Advances in Neural Information Processing Systems
    Publisher: Massachusetts Institute of Technology Press (MIT Press)
    Refereed: Yes
    URI:

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